Armatura's Facial Recognition Technology
Unlocking The Future With Precision And Protection
Introduction
The advancement of facial recognition technology to a level comparable to, or surpassing, human recognition capabilities has become a reality. The ability of humans to recognize others is based on encoding specific facial features in our memory. However, human memory is limited, making it difficult to recall vast amounts of information accurately. In the information age, it is crucial to train machines to perform recognition tasks on massive amounts of data, with facial recognition being the most effective solution, as it closely mimics the biological wayhumans recognize others.
Facial recognition Can be categorised into 1:1 and 1:N modes
1:1
verifies "you are you"
1:1
identifies "you are you "
1:1 is usually used for "identity verification", combining Individual’s ID document with facial recognition technology. The system checks the identity document, then captures the person's face in the image and extracts the unique features of the face, such as facial contours, eyes and mouth etc. The system then converts the detected face into digital signal and extracts the unique features of the face to form a biometric template. The template is then compared with the facial template converted from the face image stored in the ID document to determine whether the ID document holder is the actual person represented by the ID document.
1:N
identifies "who you are."
1:N
identifies "who you are"
1:N means “Identification mode”, which relies on an established facial information database. The system collects facial image and extracts the unique features of the face, such as facial contours, eyes and mouth etc. The system then converts the detected face into a digital signal, and extracts the unique features of the face to form a biometric template. The system then compares it with the facial information in the database to determine the identity of the person being recognized.
Development and future situation
Due to the rapid speed of facial recognition technology, detecting its fake presence from a real live person can be challenging, leading to concerns about security and privacy. This has been a significant consideration throughout the entire development cycle of facial recognition, with developers ensuring that the process and scope of data collection are authorised by users. From a technical or procedural standpoint, the entire process should be safe and controllable. In recent years, the advance of machine learning-based artificial intelligence, speed, seamlessness, and security compared to traditional methods such as card swiping, passwords, and fingerprints, facial recognition technology has been widely adopted and favoured by users in various industries for access control and management. Due to the rapid speed of facial recognition technology, detecting its fake presence from a real live person can be challenging, leading to concerns about security and privacy. This has been a significant consideration throughout the entire development cycle of facial recognition, with developers ensuring that the process and scope of data collection are authorised by users. From a technical or procedural standpoint, the entire process should be safe and controllable. In recent years, the advance of machine learning-based artificial intelligence, speed, seamlessness, and security compared to traditional methods such as card swiping, passwords, and fingerprints, facial recognition technology has been widely adopted and favoured by users in various industries for access control and management.
Facial Recognition Process
01
Facial Image
Acquisition
02
Facial Detection
03
Facial Image Pre-processing
04
Facial Feature Extraction
05
Facial Feature Comparison
Armatura's Facial Recognition Advantages
Armatura's biometric identification technology offers several advantages in facial recognition. The technology must consider three key factors: facial similarity, variation, and anti-counterfeiting capabilities. In high-capacity 1:N facial \recognition scenarios, where a large amount of facial data is stored in a database, precise facial matching requires significant computational power. This places a heavy emphasis on algorithm and hardware performance. Additionally, facial features change over time, adding to the challenges of facial recognition.
Accuracy towards Facial Variation
Armatura's facial recognition technology uses deep learning algorithms that are capable of learning and adapting to changes in facial features over time. This allows for more accurate and reliable recognition, even in scenarios where facial features may have changed due to aging, facial hair, or other factors.Another advantage of Armatura's facial recognition technology is its ability to handle variations in facial expression and pose. The technology uses advanced algorithms that can recognize faces even when they are tilted, turned, or partially obscured. This allows for more flexible and versatile deployment of the technology in various scenarios.
Anti-Counterfeiting Ability
Anti-counterfeiting in facial recognition mainly aims to prevent vulnerabilities such as photo, video, and fake body forgery
Photo attack is the most primitive and simple forgery method, which deceives the system with a simple photo. To prevent photo attacks, the algorithm can dynamically detect and identify them, for example by requiring the person being recognized to perform actions such as opening their mouth, shaking their head, or blinking.
Video attack deceives the system with a pre-recorded video. To prevent video attacks, the collected facial images will have reflections or blurs due to the screen display, which creates a certain gap from real faces. Therefore, the algorithm can also identify forged video attacks.
Fake body attack deceives the system with a simulated human-like body. It avoids the shortcomings of photo and video attacks.
However, the morphology and expression of a fake body are
significantly different from a real human face. Therefore, Armatura's research and development team continuously tests and optimizes algorithms to effectively identify fake body attacks using techniques such as liveness detection and infrared light detection, ensuring maximum protection of users' privacy and security.
Armatura Facial Recognition’s Performance
Armatura's facial recognition algorithm support
128X128px
Minimum resolution
256byte
Template size
<50 ms
Detection time
<350 ms
Template extraction time
<100 ms
Verification time
100000individual
Template capacity support
≥99.2%
When the false acceptance rate (FAR) is 0.001%, the true acceptance rate (TAR)
